Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark. andreas
Med Eng Phys. 2012 Jan;34(1):46-55. doi: 10.1016/j.medengphy.2011.06.016. Epub 2011 Jul 20.
We investigated the control of electrically induced hand grasp by natural sensors located in the skin of the index finger. A tetraplegic volunteer was implanted with an eight-channel muscle stimulator, providing hand grasp, and a nerve signal recording cuff electrode placed on a branch of the palmar digital nerve deriving from the median nerve and innervating the radial aspect of the index finger. The recorded nerve signal contained information that could be used to detect slips and further to increase the stimulation intensity to stop a slip. A mathematical model of the experiments was employed to understand the behaviour of the hand grasp system, to extrapolate data obtained in experiments, to test parameter influence on the length of a slip, and to refine the control algorithm. The model considered the mechanical conditions causing a slip, the major properties of the musculo-skeletal system of the hand in lateral grasp, and the relation between the processed nerve signal and the slip velocity. The objective of this study was to investigate the control algorithm in both experiment and simulation. We tested reactions on a slip with different fixed increases in stimulator command and an algorithm with adaptive increase in stimulator command based on the amplitude of the processed nerve signal. Further, the application of a number of maximum stimulator commands with double the instantaneous stimulation frequency as an initial reaction to a slip was investigated. We tested three different surfaces of the held object, representing different frictional conditions of the skin-object contact. A discussion of the slip-based controller concludes the paper.
我们研究了通过位于食指皮肤中的天然传感器来控制电诱导手抓握的方法。一位四肢瘫痪的志愿者被植入了一个八通道肌肉刺激器，以提供手抓握的功能，同时还放置了一个神经信号记录袖带电极，该电极位于源自正中神经并支配食指桡侧的手掌指神经分支上。记录的神经信号包含可用于检测滑动的信息，并进一步增加刺激强度以停止滑动。我们采用了一个实验数学模型来理解手抓握系统的行为，推断实验中获得的数据，测试参数对滑动长度的影响，并改进控制算法。该模型考虑了导致滑动的机械条件、手在横向抓握时的主要骨骼肌肉系统特性，以及处理后的神经信号与滑动速度之间的关系。本研究的目的是在实验和模拟中研究控制算法。我们测试了不同固定刺激器命令增量下对滑动的反应，以及基于处理后的神经信号幅度自适应增加刺激器命令的算法。此外，还研究了将瞬时刺激频率增加一倍的多个最大刺激器命令作为对滑动的初始反应的应用。我们测试了被握持物体的三个不同表面，代表了皮肤-物体接触的不同摩擦条件。本文最后讨论了基于滑动的控制器。
